Computational Biologist - Quantitative Methods & Target Discovery
Eli Lilly and Company
August 10, 2026
On-site
Boston, MA
Clinical Research and Development
The Opportunity (Individual Contributor)
Experienced computational biologist in Boston or Indianapolis (Data Science, CardioMetabolic Research), leading analyses of multimodal biological datasets and developing methods that advance target discovery in cardiometabolic diseases.
Responsibilities
- Design and implement single-cell and spatial omics analyses integrating imaging-, sequencing-, and multiplexed platforms to characterize tissue architecture, cellular neighborhoods, and system-level dynamics.
- Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics for discovery-ready data layers and downstream modeling.
- Conduct end-to-end functional genomics analyses (CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate with transcriptomic, proteomic, and pathway data for target prioritization.
- Ingest, develop, and apply advanced AI/ML, statistical, and computational frameworks to analyze single-cell, spatial transcriptomic/proteomic, metabolomic, and multi-omics datasets at scale.
- Partner with pre-clinical and translational teams to frame questions, ensure statistical rigor, and translate results into target decisions.
- Build convergent evidence frameworks using statistical genetics outputs; develop predictive models to score/rank targets and distinguish association from mechanism.
- Advance quantitative toolkit (Bayesian methods, causal inference/graphs, knowledge graphs, mechanistic/agent-based modeling) and improve reproducible, scalable analytical workflows and data architecture standards.
- Collaborate with internal AI, data engineering, translational biology, and statistics teams; champion analytical rigor and advise peers.
Minimum requirements
- Ph.D. in computational biology/biostatistics/related quantitative life science field with experience developing analytical methods (Bayesian/AI/ML) and applying them to multi-omics, spatial omics, or functional genomics.
Preferred
- 2+ years post-doc or biopharma/biotech experience.
- Spatial omics and/or single-cell RNA-seq/proteomics/metabolomics/multi-omics integration.
- Proficiency in Python and/or R; strong software practices.
- Workflow orchestration (e.g., Nextflow), cloud-native analytics, and scalable pipeline development.
- Experience with 2+ of: Bayesian methods (e.g., PyMC/Stan), causal modeling, knowledge graphs, ML/AI target discovery, causal inference, or scalable functional genomics.
- Ability to interpret statistical genetics outputs; embedded cross-functional collaboration.
- Track record of leading via scientific influence; strong publications.
Benefits
- Eligible for company bonus (depends on performance) and comprehensive benefit program including 401(k), pension, vacation, medical/dental/vision/prescription coverage, flexible benefits, life insurance, time-off/leave, and well-being benefits.
Application instructions
- If you require accommodation to submit a resume, complete the workplace accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation
Experienced computational biologist in Boston or Indianapolis (Data Science, CardioMetabolic Research), leading analyses of multimodal biological datasets and developing methods that advance target discovery in cardiometabolic diseases.
Responsibilities
- Design and implement single-cell and spatial omics analyses integrating imaging-, sequencing-, and multiplexed platforms to characterize tissue architecture, cellular neighborhoods, and system-level dynamics.
- Build scalable pipelines to preprocess, QC, harmonize, and integrate large-scale spatial and molecular omics for discovery-ready data layers and downstream modeling.
- Conduct end-to-end functional genomics analyses (CRISPR screens, perturb-seq, high-content perturbation readouts) and integrate with transcriptomic, proteomic, and pathway data for target prioritization.
- Ingest, develop, and apply advanced AI/ML, statistical, and computational frameworks to analyze single-cell, spatial transcriptomic/proteomic, metabolomic, and multi-omics datasets at scale.
- Partner with pre-clinical and translational teams to frame questions, ensure statistical rigor, and translate results into target decisions.
- Build convergent evidence frameworks using statistical genetics outputs; develop predictive models to score/rank targets and distinguish association from mechanism.
- Advance quantitative toolkit (Bayesian methods, causal inference/graphs, knowledge graphs, mechanistic/agent-based modeling) and improve reproducible, scalable analytical workflows and data architecture standards.
- Collaborate with internal AI, data engineering, translational biology, and statistics teams; champion analytical rigor and advise peers.
Minimum requirements
- Ph.D. in computational biology/biostatistics/related quantitative life science field with experience developing analytical methods (Bayesian/AI/ML) and applying them to multi-omics, spatial omics, or functional genomics.
Preferred
- 2+ years post-doc or biopharma/biotech experience.
- Spatial omics and/or single-cell RNA-seq/proteomics/metabolomics/multi-omics integration.
- Proficiency in Python and/or R; strong software practices.
- Workflow orchestration (e.g., Nextflow), cloud-native analytics, and scalable pipeline development.
- Experience with 2+ of: Bayesian methods (e.g., PyMC/Stan), causal modeling, knowledge graphs, ML/AI target discovery, causal inference, or scalable functional genomics.
- Ability to interpret statistical genetics outputs; embedded cross-functional collaboration.
- Track record of leading via scientific influence; strong publications.
Benefits
- Eligible for company bonus (depends on performance) and comprehensive benefit program including 401(k), pension, vacation, medical/dental/vision/prescription coverage, flexible benefits, life insurance, time-off/leave, and well-being benefits.
Application instructions
- If you require accommodation to submit a resume, complete the workplace accommodation request form: https://careers.lilly.com/us/en/workplace-accommodation